L2-norm multiple kernel learning and its application to biomedical data fusion

BMC Bioinformatics

Table 5 Results of experiment 1: prioritization of 620 disease relevant genes by genomic data fusion

	Error of AUC (mean)	Error of AUC (std.)	p-value	corr	corr	corr	corr
L _∞	0.0923	0.0035	2.98 · 10^-17	-	0.94	0.66	0.82
L_∞(0.5)	0.0806	0.0033	2.66 · 10^-06	0.94	-	0.82	0.92
L ₁	0.0908	0.0042	1.92 · 10^-16	0.66	0.82	-	0.90
L ₂	0.0780	0.0034	-	0.82	0.92	0.90	-

Results of experiment 1: disease relevant gene prioritization by genomic data fusion. The error of AUC values is evaluated by LOO validation in 20 random repetitions. The best performance (L₂) is shown in bold. The p-values are compared with the best performance using a paired t-test. As shown, the L₂ method is significantly better than other methods. The paired Spearman correlation scores compare similarities of rankings obtained by different approaches when compared with the target rankings (denoted as -). Higher Spearman correlation values mean that the two ranking results are much similar.

ISSN: 1471-2105